Forecasting tomorrow’s tourist

Sérgio Moro, Paulo Rita

Research output: Contribution to journalReview articlepeer-review

10 Citations (Scopus)


Purpose: This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016. Design/methodology/approach: For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe. Findings: The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques. Originality/value: The present literature review offers recent insights on tourism forecasting scientific literature, providing evidences on current trends and revealing interesting research gaps.

Original languageEnglish
Pages (from-to)643-653
Number of pages11
JournalWorldwide Hospitality and Tourism Themes
Issue number6
Publication statusPublished - 2016


  • Modeling
  • Tourism demand
  • Tourism forecasting
  • Tourism prediction
  • Tourists’ behavior


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